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Commit b8166f31 authored by rusty1s's avatar rusty1s
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linting and interface changes

parent cd7dbf25
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Showing with 517 additions and 555 deletions
csrc/cpu/utils/cloud.h 100755 → 100644
View file @ b8166f31
// Author: Peiyuan Liao (alexander_liao@outlook.com)
//
# pragma once
#pragma once
#include <ATen/ATen.h>
#include <algorithm>
#include <cmath>
#include <vector>
#include <unordered_map>
#include <iomanip>
#include <iostream>
#include <map>
#include <algorithm>
#include <numeric>
#include <iostream>
#include <iomanip>
#include <unordered_map>
#include <vector>
#include <time.h>
template<typename scalar_t>
struct PointCloud
{
std::vector<std::vector<scalar_t>*> pts;
void set(std::vector<scalar_t> new_pts, int dim){
std::vector<std::vector<scalar_t>*> temp(new_pts.size()/dim);
for(size_t i=0; i < new_pts.size(); i++){
if(i%dim == 0){
std::vector<scalar_t>* point = new std::vector<scalar_t>(dim);
for (size_t j = 0; j < (size_t)dim; j++) {
(*point)[j]=new_pts[i+j];
}
temp[i/dim] = point;
}
}
pts = temp;
}
void set_batch(std::vector<scalar_t> new_pts, size_t begin, long size, int dim){
std::vector<std::vector<scalar_t>*> temp(size);
for(size_t i=0; i < (size_t)size; i++){
std::vector<scalar_t>* point = new std::vector<scalar_t>(dim);
for (size_t j = 0; j < (size_t)dim; j++) {
(*point)[j] = new_pts[dim*(begin+i)+j];
}
temp[i] = point;
}
pts = temp;
}
// Must return the number of data points
inline size_t kdtree_get_point_count() const { return pts.size(); }
// Returns the dim'th component of the idx'th point in the class:
inline scalar_t kdtree_get_pt(const size_t idx, const size_t dim) const
{
return (*pts[idx])[dim];
}
// Optional bounding-box computation: return false to default to a standard bbox computation loop.
// Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
// Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
template <class BBOX>
bool kdtree_get_bbox(BBOX& /* bb */) const { return false; }
template <typename scalar_t> struct PointCloud {
std::vector<std::vector<scalar_t> *> pts;
void set(std::vector<scalar_t> new_pts, int dim) {
std::vector<std::vector<scalar_t> *> temp(new_pts.size() / dim);
for (size_t i = 0; i < new_pts.size(); i++) {
if (i % dim == 0) {
std::vector<scalar_t> *point = new std::vector<scalar_t>(dim);
for (size_t j = 0; j < (size_t)dim; j++) {
(*point)[j] = new_pts[i + j];
}
temp[i / dim] = point;
}
}
pts = temp;
}
void set_batch(std::vector<scalar_t> new_pts, size_t begin, long size,
int dim) {
std::vector<std::vector<scalar_t> *> temp(size);
for (size_t i = 0; i < (size_t)size; i++) {
std::vector<scalar_t> *point = new std::vector<scalar_t>(dim);
for (size_t j = 0; j < (size_t)dim; j++) {
(*point)[j] = new_pts[dim * (begin + i) + j];
}
temp[i] = point;
}
pts = temp;
}
// Must return the number of data points.
inline size_t kdtree_get_point_count() const { return pts.size(); }
// Returns the dim'th component of the idx'th point in the class:
inline scalar_t kdtree_get_pt(const size_t idx, const size_t dim) const {
return (*pts[idx])[dim];
}
// Optional bounding-box computation: return false to default to a standard
// bbox computation loop.
// Return true if the BBOX was already computed by the class and returned in
// "bb" so it can be avoided to redo it again. Look at bb.size() to find out
// the expected dimensionality (e.g. 2 or 3 for point clouds)
template <class BBOX> bool kdtree_get_bbox(BBOX & /* bb */) const {
return false;
}
};
csrc/cpu/utils/neighbors.cpp 100755 → 100644
View file @ b8166f31
#include "cloud.h"
#include "nanoflann.hpp"
#include <set>
#include <cstdint>
#include <thread>
#include <iostream>
#include <set>
#include <thread>
typedef struct thread_struct {
void* kd_tree;
void* matches;
void* queries;
size_t* max_count;
std::mutex* ct_m;
std::mutex* tree_m;
size_t start;
size_t end;
double search_radius;
bool small;
bool option;
size_t k;
void *kd_tree;
void *matches;
void *queries;
size_t *max_count;
std::mutex *ct_m;
std::mutex *tree_m;
size_t start;
size_t end;
double search_radius;
bool small;
bool option;
size_t k;
} thread_args;
template<typename scalar_t>
void thread_routine(thread_args* targs) {
typedef nanoflann::KDTreeSingleIndexAdaptor< nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t> > , PointCloud<scalar_t>> my_kd_tree_t;
typedef std::vector< std::vector<std::pair<size_t, scalar_t> > > kd_pair;
my_kd_tree_t* index = (my_kd_tree_t*) targs->kd_tree;
kd_pair* matches = (kd_pair*)targs->matches;
PointCloud<scalar_t>* pcd_query = (PointCloud<scalar_t>*)targs->queries;
size_t* max_count = targs->max_count;
std::mutex* ct_m = targs->ct_m;
std::mutex* tree_m = targs->tree_m;
double eps;
if (targs->small) {
eps = 0.000001;
}
else {
eps = 0;
}
double search_radius = (double) targs->search_radius;
size_t start = targs->start;
size_t end = targs->end;
auto k = targs->k;
for (size_t i = start; i < end; i++) {
std::vector<scalar_t> p0 = *(((*pcd_query).pts)[i]);
scalar_t* query_pt = new scalar_t[p0.size()];
std::copy(p0.begin(), p0.end(), query_pt);
(*matches)[i].reserve(*max_count);
std::vector<std::pair<size_t, scalar_t> > ret_matches;
std::vector<size_t>* knn_ret_matches = new std::vector<size_t>(k);
std::vector<scalar_t>* knn_dist_matches = new std::vector<scalar_t>(k);
tree_m->lock();
size_t nMatches;
if (targs->option){
nMatches = index->radiusSearch(query_pt, (scalar_t)(search_radius+eps), ret_matches, nanoflann::SearchParams());
}
else {
nMatches = index->knnSearch(query_pt, k, &(*knn_ret_matches)[0],&(* knn_dist_matches)[0]);
auto temp = new std::vector<std::pair<size_t, scalar_t> >((*knn_dist_matches).size());
for (size_t j = 0; j < (*knn_ret_matches).size(); j++){
(*temp)[j] = std::make_pair( (*knn_ret_matches)[j],(*knn_dist_matches)[j] );
}
ret_matches = *temp;
}
tree_m->unlock();
(*matches)[i] = ret_matches;
ct_m->lock();
if(*max_count < nMatches) {
*max_count = nMatches;
}
ct_m->unlock();
}
template <typename scalar_t> void thread_routine(thread_args *targs) {
typedef nanoflann::KDTreeSingleIndexAdaptor<
nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t>>,
PointCloud<scalar_t>>
my_kd_tree_t;
typedef std::vector<std::vector<std::pair<size_t, scalar_t>>> kd_pair;
my_kd_tree_t *index = (my_kd_tree_t *)targs->kd_tree;
kd_pair *matches = (kd_pair *)targs->matches;
PointCloud<scalar_t> *pcd_query = (PointCloud<scalar_t> *)targs->queries;
size_t *max_count = targs->max_count;
std::mutex *ct_m = targs->ct_m;
std::mutex *tree_m = targs->tree_m;
double eps;
if (targs->small) {
eps = 0.000001;
} else {
eps = 0;
}
double search_radius = (double)targs->search_radius;
size_t start = targs->start;
size_t end = targs->end;
auto k = targs->k;
for (size_t i = start; i < end; i++) {
std::vector<scalar_t> p0 = *(((*pcd_query).pts)[i]);
scalar_t *query_pt = new scalar_t[p0.size()];
std::copy(p0.begin(), p0.end(), query_pt);
(*matches)[i].reserve(*max_count);
std::vector<std::pair<size_t, scalar_t>> ret_matches;
std::vector<size_t> *knn_ret_matches = new std::vector<size_t>(k);
std::vector<scalar_t> *knn_dist_matches = new std::vector<scalar_t>(k);
tree_m->lock();
size_t nMatches;
if (targs->option) {
nMatches = index->radiusSearch(query_pt, (scalar_t)(search_radius + eps),
ret_matches, nanoflann::SearchParams());
} else {
nMatches = index->knnSearch(query_pt, k, &(*knn_ret_matches)[0],
&(*knn_dist_matches)[0]);
auto temp = new std::vector<std::pair<size_t, scalar_t>>(
(*knn_dist_matches).size());
for (size_t j = 0; j < (*knn_ret_matches).size(); j++) {
(*temp)[j] =
std::make_pair((*knn_ret_matches)[j], (*knn_dist_matches)[j]);
}
ret_matches = *temp;
}
tree_m->unlock();
(*matches)[i] = ret_matches;
ct_m->lock();
if (*max_count < nMatches) {
*max_count = nMatches;
}
ct_m->unlock();
}
}
template<typename scalar_t>
size_t nanoflann_neighbors(std::vector<scalar_t>& queries, std::vector<scalar_t>& supports,
std::vector<size_t>*& neighbors_indices, double radius, int dim,
int64_t max_num, int64_t n_threads, int64_t k, int option){
const scalar_t search_radius = static_cast<scalar_t>(radius*radius);
// Counting vector
size_t* max_count = new size_t();
*max_count = 1;
size_t ssize = supports.size();
// CLoud variable
PointCloud<scalar_t> pcd;
pcd.set(supports, dim);
// Cloud query
PointCloud<scalar_t>* pcd_query = new PointCloud<scalar_t>();
(*pcd_query).set(queries, dim);
// Tree parameters
nanoflann::KDTreeSingleIndexAdaptorParams tree_params(15 /* max leaf */);
// KDTree type definition
typedef nanoflann::KDTreeSingleIndexAdaptor< nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t> > , PointCloud<scalar_t>> my_kd_tree_t;
typedef std::vector< std::vector<std::pair<size_t, scalar_t> > > kd_pair;
// Pointer to trees
my_kd_tree_t* index;
index = new my_kd_tree_t(dim, pcd, tree_params);
index->buildIndex();
// Search neigbors indices
// Search params
nanoflann::SearchParams search_params;
// search_params.sorted = true;
kd_pair* list_matches = new kd_pair((*pcd_query).pts.size());
// single threaded routine
if (n_threads == 1){
size_t i0 = 0;
double eps;
if (ssize < 10) {
eps = 0.000001;
}
else {
eps = 0;
}
for (auto& p : (*pcd_query).pts){
auto p0 = *p;
// Find neighbors
scalar_t* query_pt = new scalar_t[dim];
std::copy(p0.begin(), p0.end(), query_pt);
(*list_matches)[i0].reserve(*max_count);
std::vector<std::pair<size_t, scalar_t> > ret_matches;
std::vector<size_t>* knn_ret_matches = new std::vector<size_t>(k);
std::vector<scalar_t>* knn_dist_matches = new std::vector<scalar_t>(k);
size_t nMatches;
if (!!(option)){
nMatches = index->radiusSearch(query_pt, (scalar_t)(search_radius+eps), ret_matches, search_params);
}
else {
nMatches = index->knnSearch(query_pt, (size_t)k, &(*knn_ret_matches)[0],&(* knn_dist_matches)[0]);
auto temp = new std::vector<std::pair<size_t, scalar_t> >((*knn_dist_matches).size());
for (size_t j = 0; j < (*knn_ret_matches).size(); j++){
(*temp)[j] = std::make_pair( (*knn_ret_matches)[j],(*knn_dist_matches)[j] );
}
ret_matches = *temp;
}
(*list_matches)[i0] = ret_matches;
if(*max_count < nMatches) *max_count = nMatches;
i0++;
}
}
else {// Multi-threaded routine
std::mutex* mtx = new std::mutex();
std::mutex* mtx_tree = new std::mutex();
size_t n_queries = (*pcd_query).pts.size();
size_t actual_threads = std::min((long long)n_threads, (long long)n_queries);
std::vector<std::thread*> tid(actual_threads);
size_t start, end;
size_t length;
if (n_queries) {
length = 1;
}
else {
auto res = std::lldiv((long long)n_queries, (long long)n_threads);
length = (size_t)res.quot;
}
for (size_t t = 0; t < actual_threads; t++) {
start = t*length;
if (t == actual_threads-1) {
end = n_queries;
}
else {
end = (t+1)*length;
}
thread_args* targs = new thread_args();
targs->kd_tree = index;
targs->matches = list_matches;
targs->max_count = max_count;
targs->ct_m = mtx;
targs->tree_m = mtx_tree;
targs->search_radius = search_radius;
targs->queries = pcd_query;
targs->start = start;
targs->end = end;
if (ssize < 10) {
targs->small = true;
}
else {
targs->small = false;
}
targs->option = !!(option);
targs->k = k;
std::thread* temp = new std::thread(thread_routine<scalar_t>, targs);
tid[t] = temp;
}
for (size_t t = 0; t < actual_threads; t++){
tid[t]->join();
}
}
// Reserve the memory
if(max_num > 0) {
*max_count = max_num;
}
size_t size = 0; // total number of edges
for (auto& inds : *list_matches){
if(inds.size() <= *max_count)
size += inds.size();
else
size += *max_count;
}
neighbors_indices->resize(size*2);
size_t i1 = 0; // index of the query points
size_t u = 0; // curent index of the neighbors_indices
for (auto& inds : *list_matches){
for (size_t j = 0; j < *max_count; j++){
if(j < inds.size()){
(*neighbors_indices)[u] = inds[j].first;
(*neighbors_indices)[u + 1] = i1;
u += 2;
}
}
i1++;
}
return *max_count;
template <typename scalar_t>
size_t nanoflann_neighbors(std::vector<scalar_t> &queries,
std::vector<scalar_t> &supports,
std::vector<size_t> *&neighbors_indices,
double radius, int dim, int64_t max_num,
int64_t n_threads, int64_t k, int option) {
const scalar_t search_radius = static_cast<scalar_t>(radius * radius);
// Counting vector
size_t *max_count = new size_t();
*max_count = 1;
size_t ssize = supports.size();
// CLoud variable
PointCloud<scalar_t> pcd;
pcd.set(supports, dim);
// Cloud query
PointCloud<scalar_t> *pcd_query = new PointCloud<scalar_t>();
(*pcd_query).set(queries, dim);
// Tree parameters
nanoflann::KDTreeSingleIndexAdaptorParams tree_params(15 /* max leaf */);
// KDTree type definition
typedef nanoflann::KDTreeSingleIndexAdaptor<
nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t>>,
PointCloud<scalar_t>>
my_kd_tree_t;
typedef std::vector<std::vector<std::pair<size_t, scalar_t>>> kd_pair;
// Pointer to trees
my_kd_tree_t *index;
index = new my_kd_tree_t(dim, pcd, tree_params);
index->buildIndex();
// Search neigbors indices
// Search params
nanoflann::SearchParams search_params;
// search_params.sorted = true;
kd_pair *list_matches = new kd_pair((*pcd_query).pts.size());
// single threaded routine
if (n_threads == 1) {
size_t i0 = 0;
double eps;
if (ssize < 10) {
eps = 0.000001;
} else {
eps = 0;
}
for (auto &p : (*pcd_query).pts) {
auto p0 = *p;
// Find neighbors
scalar_t *query_pt = new scalar_t[dim];
std::copy(p0.begin(), p0.end(), query_pt);
(*list_matches)[i0].reserve(*max_count);
std::vector<std::pair<size_t, scalar_t>> ret_matches;
std::vector<size_t> *knn_ret_matches = new std::vector<size_t>(k);
std::vector<scalar_t> *knn_dist_matches = new std::vector<scalar_t>(k);
size_t nMatches;
if (!!(option)) {
nMatches =
index->radiusSearch(query_pt, (scalar_t)(search_radius + eps),
ret_matches, search_params);
} else {
nMatches = index->knnSearch(query_pt, (size_t)k, &(*knn_ret_matches)[0],
&(*knn_dist_matches)[0]);
auto temp = new std::vector<std::pair<size_t, scalar_t>>(
(*knn_dist_matches).size());
for (size_t j = 0; j < (*knn_ret_matches).size(); j++) {
(*temp)[j] =
std::make_pair((*knn_ret_matches)[j], (*knn_dist_matches)[j]);
}
ret_matches = *temp;
}
(*list_matches)[i0] = ret_matches;
if (*max_count < nMatches)
*max_count = nMatches;
i0++;
}
} else { // Multi-threaded routine
std::mutex *mtx = new std::mutex();
std::mutex *mtx_tree = new std::mutex();
size_t n_queries = (*pcd_query).pts.size();
size_t actual_threads =
std::min((long long)n_threads, (long long)n_queries);
std::vector<std::thread *> tid(actual_threads);
size_t start, end;
size_t length;
if (n_queries) {
length = 1;
} else {
auto res = std::lldiv((long long)n_queries, (long long)n_threads);
length = (size_t)res.quot;
}
for (size_t t = 0; t < actual_threads; t++) {
start = t * length;
if (t == actual_threads - 1) {
end = n_queries;
} else {
end = (t + 1) * length;
}
thread_args *targs = new thread_args();
targs->kd_tree = index;
targs->matches = list_matches;
targs->max_count = max_count;
targs->ct_m = mtx;
targs->tree_m = mtx_tree;
targs->search_radius = search_radius;
targs->queries = pcd_query;
targs->start = start;
targs->end = end;
if (ssize < 10) {
targs->small = true;
} else {
targs->small = false;
}
targs->option = !!(option);
targs->k = k;
std::thread *temp = new std::thread(thread_routine<scalar_t>, targs);
tid[t] = temp;
}
for (size_t t = 0; t < actual_threads; t++) {
tid[t]->join();
}
}
// Reserve the memory
if (max_num > 0) {
*max_count = max_num;
}
size_t size = 0; // total number of edges
for (auto &inds : *list_matches) {
if (inds.size() <= *max_count)
size += inds.size();
else
size += *max_count;
}
neighbors_indices->resize(size * 2);
size_t i1 = 0; // index of the query points
size_t u = 0; // curent index of the neighbors_indices
for (auto &inds : *list_matches) {
for (size_t j = 0; j < *max_count; j++) {
if (j < inds.size()) {
(*neighbors_indices)[u] = inds[j].first;
(*neighbors_indices)[u + 1] = i1;
u += 2;
}
}
i1++;
}
return *max_count;
}
template<typename scalar_t>
size_t batch_nanoflann_neighbors (std::vector<scalar_t>& queries,
std::vector<scalar_t>& supports,
std::vector<long>& q_batches,
std::vector<long>& s_batches,
std::vector<size_t>*& neighbors_indices,
double radius, int dim, int64_t max_num, int64_t k, int option){
// indices
size_t i0 = 0;
// Square radius
const scalar_t r2 = static_cast<scalar_t>(radius*radius);
// Counting vector
size_t max_count = 0;
// batch index
size_t b = 0;
size_t sum_qb = 0;
size_t sum_sb = 0;
double eps;
if (supports.size() < 10){
eps = 0.000001;
}
else {
eps = 0;
}
// Nanoflann related variables
// CLoud variable
PointCloud<scalar_t> current_cloud;
PointCloud<scalar_t> query_pcd;
query_pcd.set(queries, dim);
std::vector<std::vector<std::pair<size_t, scalar_t> > > all_inds_dists(query_pcd.pts.size());
// Tree parameters
nanoflann::KDTreeSingleIndexAdaptorParams tree_params(10 /* max leaf */);
// KDTree type definition
typedef nanoflann::KDTreeSingleIndexAdaptor< nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t> > , PointCloud<scalar_t>> my_kd_tree_t;
// Pointer to trees
my_kd_tree_t* index;
// Build KDTree for the first batch element
current_cloud.set_batch(supports, sum_sb, s_batches[b], dim);
index = new my_kd_tree_t(dim, current_cloud, tree_params);
index->buildIndex();
// Search neigbors indices
// Search params
nanoflann::SearchParams search_params;
search_params.sorted = true;
for (auto& p : query_pcd.pts){
auto p0 = *p;
// Check if we changed batch
scalar_t* query_pt = new scalar_t[dim];
std::copy(p0.begin(), p0.end(), query_pt);
if (i0 == sum_qb + q_batches[b]){
sum_qb += q_batches[b];
sum_sb += s_batches[b];
b++;
// Change the points
current_cloud.pts.clear();
current_cloud.set_batch(supports, sum_sb, s_batches[b], dim);
// Build KDTree of the current element of the batch
delete index;
index = new my_kd_tree_t(dim, current_cloud, tree_params);
index->buildIndex();
}
// Initial guess of neighbors size
all_inds_dists[i0].reserve(max_count);
// Find neighbors
size_t nMatches;
if (!!option) {
nMatches = index->radiusSearch(query_pt, r2+eps, all_inds_dists[i0], search_params);
// Update max count
}
else {
std::vector<size_t>* knn_ret_matches = new std::vector<size_t>(k);
std::vector<scalar_t>* knn_dist_matches = new std::vector<scalar_t>(k);
nMatches = index->knnSearch(query_pt, (size_t)k, &(*knn_ret_matches)[0],&(*knn_dist_matches)[0]);
auto temp = new std::vector<std::pair<size_t, scalar_t> >((*knn_dist_matches).size());
for (size_t j = 0; j < (*knn_ret_matches).size(); j++){
(*temp)[j] = std::make_pair( (*knn_ret_matches)[j],(*knn_dist_matches)[j] );
}
all_inds_dists[i0] = *temp;
}
if (nMatches > max_count)
max_count = nMatches;
// Increment query idx
i0++;
}
// how many neighbors do we keep
if(max_num > 0) {
max_count = max_num;
}
// Reserve the memory
size_t size = 0; // total number of edges
for (auto& inds_dists : all_inds_dists){
if(inds_dists.size() <= max_count)
size += inds_dists.size();
else
size += max_count;
}
neighbors_indices->resize(size * 2);
i0 = 0;
sum_sb = 0;
sum_qb = 0;
b = 0;
size_t u = 0;
for (auto& inds_dists : all_inds_dists){
if (i0 == sum_qb + q_batches[b]){
sum_qb += q_batches[b];
sum_sb += s_batches[b];
b++;
}
for (size_t j = 0; j < max_count; j++){
if (j < inds_dists.size()){
(*neighbors_indices)[u] = inds_dists[j].first + sum_sb;
(*neighbors_indices)[u + 1] = i0;
u += 2;
}
}
i0++;
}
return max_count;
template <typename scalar_t>
size_t batch_nanoflann_neighbors(std::vector<scalar_t> &queries,
std::vector<scalar_t> &supports,
std::vector<long> &q_batches,
std::vector<long> &s_batches,
std::vector<size_t> *&neighbors_indices,
double radius, int dim, int64_t max_num,
int64_t k, int option) {
// Indices.
size_t i0 = 0;
// Square radius.
const scalar_t r2 = static_cast<scalar_t>(radius * radius);
// Counting vector.
size_t max_count = 0;
// Batch index.
size_t b = 0;
size_t sum_qb = 0;
size_t sum_sb = 0;
double eps;
if (supports.size() < 10) {
eps = 0.000001;
} else {
eps = 0;
}
// Nanoflann related variables.
// Cloud variable.
PointCloud<scalar_t> current_cloud;
PointCloud<scalar_t> query_pcd;
query_pcd.set(queries, dim);
std::vector<std::vector<std::pair<size_t, scalar_t>>> all_inds_dists(
query_pcd.pts.size());
// Tree parameters.
nanoflann::KDTreeSingleIndexAdaptorParams tree_params(10 /* max leaf */);
// KDTree type definition.
typedef nanoflann::KDTreeSingleIndexAdaptor<
nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t>>,
PointCloud<scalar_t>>
my_kd_tree_t;
// Pointer to trees.
my_kd_tree_t *index;
// Build KDTree for the first batch element.
current_cloud.set_batch(supports, sum_sb, s_batches[b], dim);
index = new my_kd_tree_t(dim, current_cloud, tree_params);
index->buildIndex();
// Search neigbors indices.
// Search params.
nanoflann::SearchParams search_params;
search_params.sorted = true;
for (auto &p : query_pcd.pts) {
auto p0 = *p;
// Check if we changed batch.
scalar_t *query_pt = new scalar_t[dim];
std::copy(p0.begin(), p0.end(), query_pt);
if (i0 == sum_qb + q_batches[b]) {
sum_qb += q_batches[b];
sum_sb += s_batches[b];
b++;
// Change the points.
current_cloud.pts.clear();
current_cloud.set_batch(supports, sum_sb, s_batches[b], dim);
// Build KDTree of the current element of the batch.
delete index;
index = new my_kd_tree_t(dim, current_cloud, tree_params);
index->buildIndex();
}
// Initial guess of neighbors size.
all_inds_dists[i0].reserve(max_count);
// Find neighbors.
size_t nMatches;
if (!!option) {
nMatches = index->radiusSearch(query_pt, r2 + eps, all_inds_dists[i0],
search_params);
// Update max count.
} else {
std::vector<size_t> *knn_ret_matches = new std::vector<size_t>(k);
std::vector<scalar_t> *knn_dist_matches = new std::vector<scalar_t>(k);
nMatches = index->knnSearch(query_pt, (size_t)k, &(*knn_ret_matches)[0],
&(*knn_dist_matches)[0]);
auto temp = new std::vector<std::pair<size_t, scalar_t>>(
(*knn_dist_matches).size());
for (size_t j = 0; j < (*knn_ret_matches).size(); j++) {
(*temp)[j] =
std::make_pair((*knn_ret_matches)[j], (*knn_dist_matches)[j]);
}
all_inds_dists[i0] = *temp;
}
if (nMatches > max_count)
max_count = nMatches;
i0++;
}
// How many neighbors do we keep.
if (max_num > 0) {
max_count = max_num;
}
size_t size = 0; // Total number of edges.
for (auto &inds_dists : all_inds_dists) {
if (inds_dists.size() <= max_count)
size += inds_dists.size();
else
size += max_count;
}
neighbors_indices->resize(size * 2);
i0 = 0;
sum_sb = 0;
sum_qb = 0;
b = 0;
size_t u = 0;
for (auto &inds_dists : all_inds_dists) {
if (i0 == sum_qb + q_batches[b]) {
sum_qb += q_batches[b];
sum_sb += s_batches[b];
b++;
}
for (size_t j = 0; j < max_count; j++) {
if (j < inds_dists.size()) {
(*neighbors_indices)[u] = inds_dists[j].first + sum_sb;
(*neighbors_indices)[u + 1] = i0;
u += 2;
}
}
i0++;
}
return max_count;
}
torch_cluster/knn.py
View file @ b8166f31
from typing import Optional
import torch
import numpy as np
@torch.jit.script
def knn(x: torch.Tensor, y: torch.Tensor, k: int,
batch_x: Optional[torch.Tensor] = None,
batch_y: Optional[torch.Tensor] = None,
cosine: bool = False, n_threads: int = 1) -> torch.Tensor:
batch_y: Optional[torch.Tensor] = None, cosine: bool = False,
num_workers: int = 1) -> torch.Tensor:
r"""Finds for each element in :obj:`y` the :obj:`k` nearest points in
:obj:`x`.
......@@ -19,13 +19,18 @@ def knn(x: torch.Tensor, y: torch.Tensor, k: int,
k (int): The number of neighbors.
batch_x (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
node to a specific example. (default: :obj:`None`)
node to a specific example. :obj:`batch_x` needs to be sorted.
(default: :obj:`None`)
batch_y (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^M`, which assigns each
node to a specific example. (default: :obj:`None`)
cosine (boolean, optional): If :obj:`True`, will use the cosine
distance instead of euclidean distance to find nearest neighbors.
(default: :obj:`False`)
node to a specific example. :obj:`batch_y` needs to be sorted.
(default: :obj:`None`)
cosine (boolean, optional): If :obj:`True`, will use the Cosine
distance instead of the Euclidean distance to find nearest
neighbors. (default: :obj:`False`)
num_workers (int): Number of workers to use for computation. Has no
effect in case :obj:`batch_x` or :obj:`batch_y` is not
:obj:`None`, or the input lies on the GPU. (default: :obj:`1`)
:rtype: :class:`LongTensor`
......@@ -44,62 +49,36 @@ def knn(x: torch.Tensor, y: torch.Tensor, k: int,
x = x.view(-1, 1) if x.dim() == 1 else x
y = y.view(-1, 1) if y.dim() == 1 else y
def is_sorted(x):
return (np.diff(x.detach().cpu()) >= 0).all()
if x.is_cuda:
if batch_x is not None:
assert x.size(0) == batch_x.numel()
assert is_sorted(batch_x)
batch_size = int(batch_x.max()) + 1
if batch_x is not None:
assert x.size(0) == batch_x.numel()
batch_size = int(batch_x.max()) + 1
deg = x.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_x, torch.ones_like(batch_x))
deg = x.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_x, torch.ones_like(batch_x))
ptr_x = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_x[1:])
else:
ptr_x = torch.tensor([0, x.size(0)], device=x.device)
ptr_x = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_x[1:])
if batch_y is not None:
assert y.size(0) == batch_y.numel()
assert is_sorted(batch_y)
batch_size = int(batch_y.max()) + 1
if batch_y is not None:
assert y.size(0) == batch_y.numel()
batch_size = int(batch_y.max()) + 1
deg = y.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_y, torch.ones_like(batch_y))
deg = y.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_y, torch.ones_like(batch_y))
ptr_y = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_y[1:])
else:
ptr_y = torch.tensor([0, y.size(0)], device=y.device)
return torch.ops.torch_cluster.knn(x, y, ptr_x,
ptr_y, k, cosine, n_threads)
ptr_y = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_y[1:])
else:
assert x.dim() == 2
if batch_x is not None:
assert batch_x.dim() == 1
assert is_sorted(batch_x)
assert x.size(0) == batch_x.size(0)
assert y.dim() == 2
if batch_y is not None:
assert batch_y.dim() == 1
assert is_sorted(batch_y)
assert y.size(0) == batch_y.size(0)
assert x.size(1) == y.size(1)
if cosine:
raise NotImplementedError('`cosine` argument not supported on CPU')
ptr_y = torch.tensor([0, y.size(0)], device=y.device)
return torch.ops.torch_cluster.knn(x, y, batch_x, batch_y,
k, cosine, n_threads)
return torch.ops.torch_cluster.knn(x, y, ptr_x, ptr_y, k, cosine,
num_workers)
@torch.jit.script
def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None,
loop: bool = False, flow: str = 'source_to_target',
cosine: bool = False, n_threads: int = 1) -> torch.Tensor:
cosine: bool = False, num_workers: int = 1) -> torch.Tensor:
r"""Computes graph edges to the nearest :obj:`k` points.
Args:
......@@ -108,7 +87,8 @@ def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None,
k (int): The number of neighbors.
batch (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
node to a specific example. (default: :obj:`None`)
node to a specific example. :obj:`batch` needs to be sorted.
(default: :obj:`None`)
loop (bool, optional): If :obj:`True`, the graph will contain
self-loops. (default: :obj:`False`)
flow (string, optional): The flow direction when using in combination
......@@ -117,6 +97,9 @@ def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None,
cosine (boolean, optional): If :obj:`True`, will use the cosine
distance instead of euclidean distance to find nearest neighbors.
(default: :obj:`False`)
num_workers (int): Number of workers to use for computation. Has no
effect in case :obj:`batch` is not :obj:`None`, or the input lies
on the GPU. (default: :obj:`1`)
:rtype: :class:`LongTensor`
......@@ -131,8 +114,8 @@ def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None,
"""
assert flow in ['source_to_target', 'target_to_source']
row, col = knn(x, x, k if loop else k + 1, batch, batch,
cosine=cosine, n_threads=n_threads)
row, col = knn(x, x, k if loop else k + 1, batch, batch, cosine,
num_workers)
row, col = (col, row) if flow == 'source_to_target' else (row, col)
if not loop:
mask = row != col
......
torch_cluster/radius.py
View file @ b8166f31
from typing import Optional
import torch
import numpy as np
@torch.jit.script
def radius(x: torch.Tensor, y: torch.Tensor, r: float,
batch_x: Optional[torch.Tensor] = None,
batch_y: Optional[torch.Tensor] = None,
max_num_neighbors: int = 32, n_threads: int = 1) -> torch.Tensor:
batch_y: Optional[torch.Tensor] = None, max_num_neighbors: int = 32,
num_workers: int = 1) -> torch.Tensor:
r"""Finds for each element in :obj:`y` all points in :obj:`x` within
distance :obj:`r`.
......@@ -16,17 +17,19 @@ def radius(x: torch.Tensor, y: torch.Tensor, r: float,
y (Tensor): Node feature matrix
:math:`\mathbf{Y} \in \mathbb{R}^{M \times F}`.
r (float): The radius.
batch_x (LongTensor, optional): Batch vector (must be sorted)
batch_x (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
node to a specific example. (default: :obj:`None`)
batch_y (LongTensor, optional): Batch vector (must be sorted)
node to a specific example. :obj:`batch_x` needs to be sorted.
(default: :obj:`None`)
batch_y (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^M`, which assigns each
node to a specific example. (default: :obj:`None`)
node to a specific example. :obj:`batch_y` needs to be sorted.
(default: :obj:`None`)
max_num_neighbors (int, optional): The maximum number of neighbors to
return for each element in :obj:`y`. (default: :obj:`32`)
n_threads (int): number of threads when the input is on CPU. Note
that this has no effect when batch_x or batch_y is not None, or
x is on GPU. (default: :obj:`1`)
num_workers (int): Number of workers to use for computation. Has no
effect in case :obj:`batch_x` or :obj:`batch_y` is not
:obj:`None`, or the input lies on the GPU. (default: :obj:`1`)
.. code-block:: python
......@@ -43,71 +46,49 @@ def radius(x: torch.Tensor, y: torch.Tensor, r: float,
x = x.view(-1, 1) if x.dim() == 1 else x
y = y.view(-1, 1) if y.dim() == 1 else y
def is_sorted(x):
return (np.diff(x.detach().cpu()) >= 0).all()
if x.is_cuda:
if batch_x is not None:
assert x.size(0) == batch_x.numel()
assert is_sorted(batch_x)
batch_size = int(batch_x.max()) + 1
deg = x.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_x, torch.ones_like(batch_x))
ptr_x = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_x[1:])
else:
ptr_x = None
if batch_y is not None:
assert y.size(0) == batch_y.numel()
assert is_sorted(batch_y)
batch_size = int(batch_y.max()) + 1
deg = y.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_y, torch.ones_like(batch_y))
ptr_y = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_y[1:])
else:
ptr_y = None
result = torch.ops.torch_cluster.radius(x, y, ptr_x, ptr_y, r,
max_num_neighbors, n_threads)
if batch_x is not None:
assert x.size(0) == batch_x.numel()
batch_size = int(batch_x.max()) + 1
deg = x.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_x, torch.ones_like(batch_x))
ptr_x = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_x[1:])
else:
assert x.dim() == 2
if batch_x is not None:
assert batch_x.dim() == 1
assert is_sorted(batch_x)
assert x.size(0) == batch_x.size(0)
ptr_x = None
assert y.dim() == 2
if batch_y is not None:
assert batch_y.dim() == 1
assert is_sorted(batch_y)
assert y.size(0) == batch_y.size(0)
assert x.size(1) == y.size(1)
if batch_y is not None:
assert y.size(0) == batch_y.numel()
batch_size = int(batch_y.max()) + 1
result = torch.ops.torch_cluster.radius(x, y, batch_x, batch_y, r,
max_num_neighbors, n_threads)
deg = y.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_y, torch.ones_like(batch_y))
ptr_y = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_y[1:])
else:
ptr_y = None
return result
return torch.ops.torch_cluster.radius(x, y, ptr_x, ptr_y, r,
max_num_neighbors, num_workers)
@torch.jit.script
def radius_graph(x: torch.Tensor, r: float,
batch: Optional[torch.Tensor] = None, loop: bool = False,
max_num_neighbors: int = 32,
flow: str = 'source_to_target',
n_threads: int = 1) -> torch.Tensor:
max_num_neighbors: int = 32, flow: str = 'source_to_target',
num_workers: int = 1) -> torch.Tensor:
r"""Computes graph edges to all points within a given distance.
Args:
x (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
r (float): The radius.
batch (LongTensor, optional): Batch vector (must be sorted)
batch (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
node to a specific example. (default: :obj:`None`)
node to a specific example. :obj:`batch` needs to be sorted.
(default: :obj:`None`)
loop (bool, optional): If :obj:`True`, the graph will contain
self-loops. (default: :obj:`False`)
max_num_neighbors (int, optional): The maximum number of neighbors to
......@@ -115,9 +96,9 @@ def radius_graph(x: torch.Tensor, r: float,
flow (string, optional): The flow direction when using in combination
with message passing (:obj:`"source_to_target"` or
:obj:`"target_to_source"`). (default: :obj:`"source_to_target"`)
n_threads (int): number of threads when the input is on CPU. Note
that this has no effect when batch_x or batch_y is not None, or
x is on GPU. (default: :obj:`1`)
num_workers (int): Number of workers to use for computation. Has no
effect in case :obj:`batch` is not :obj:`None`, or the input lies
on the GPU. (default: :obj:`1`)
:rtype: :class:`LongTensor`
......@@ -134,7 +115,7 @@ def radius_graph(x: torch.Tensor, r: float,
assert flow in ['source_to_target', 'target_to_source']
row, col = radius(x, x, r, batch, batch,
max_num_neighbors if loop else max_num_neighbors + 1,
n_threads)
num_workers)
row, col = (col, row) if flow == 'source_to_target' else (row, col)
if not loop:
mask = row != col
......
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